Data Visualization with R

Workshop goals

• learn about various packages for making data visualizations in R
• be able to determine which package to use
• understand basic syntax for the popular packages
• learn basic design principles for effective communication
• gain hands-on practice making different visualization types

What is R?

R is a software environment for statistical computing and graphics. Using R you can do rigorous statistical analysis, clean and manipulate data, and create publication-quality graphics.

clustering map

Popularity of R

Source: Stephen Cass, “The 2016 Top Programming Langauges”, IEEE Spectrum

R packages

Packages are programs that you import into R to help make tasks easier. The most popular R packages for working with data include dplyr, stringr, tidyr, and ggplot2.

How to find a package

There’s no easy way (yet) for new R users to find R packages that they might need. People are working on this problem. In the meantime, consult the following list or ask a Librarian!

Resources include:

• Google (“Top R packages for ….”)
• Looking at trending R packages on rdocumentation.org

Popular R packages for data viz

• ggplot2
• ggvis
• htmlwidgets (for interactive web visualizations):
• highcharter
• leaflet
• plotly
• shiny

Core R functions for plotting

You can create graphs in R without installing a package, but packages will allow you to create better visualizations that are any of the following:

• attractive
• publication-quality
• interactive

ggplot2

ggplot2 is the most popular visualization package for R. It’s the best all-purpose package for creating many types of 2-dimensional visualizations.

Source: r-statistics.co

highcharter

highcharter is an R package known as an htmlwidget, which allows you to use popular Javascript packages for visualization in R. Free unless you are using it for a commercial or government purpose.

data(citytemp)

hc <- highchart() %>% 
  hc_xAxis(categories = citytemp$month) %>% 
  hc_add_series(name = "Tokyo", data = citytemp$tokyo) %>% 
  hc_add_series(name = "London", data = citytemp$london) %>% 
  hc_add_series(name = "Other city",
                data = (citytemp$tokyo + citytemp$london)/2)

hc

leaflet

m <- leaflet(options = leafletOptions(zoomControl = FALSE, dragging=FALSE, minZoom = 15, maxZoom = 15)) %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=-78.6697, lat=35.7876,
    popup="Hello World!")
m  # Print the map

plotly

p <- plot_ly(economics, x = ~date, y = ~unemploy / pop)
p

Deciding on the right package

• static or interactive
• single viz or dashboard
• the nature of the data: geospatial? quantitative? qualitative?
• purpose and licensing

Recommended packages

• 2D vizzes: ggplot2
• 3D vizzes: rgl
• maps: leaflet
• interactive: ggvis, rbokeh, plotly, or highcharter
• network graphs: diagrammeR, visNetwork
• web applications: shiny

ggplot2: the most important package to learn first

ggplot2 was created on the principles of the Layered Grammar of Graphics (2010), by Hadley Wickham and based of off work from Wilkinson, Anand, & Grossman (2005) and Jaques Bertin (1983).

Essentially: graphs are like sentences you can construct, and they have a grammar. The grammar of graphics consists of the following:

at least one layer:

• data & aesthetic mapping of the data to variables
• a statistical transformation
• geometric object (geom) – point, line, bar, etc
• position adjustment

scale
coordinate system
facet (optional)

These components make up a graph.

Open script.R file

Download the following file: script.R Click the blue download button
Open RStudio. File > Open File…
Select the script.R file that you just downloaded (probably in your Downloads folder) Click Open

Get to know the data

Let’s see an example of a simple graph created with ggplot. We are going to use the mpg data set about different cars and their properties.

Exercise #1: In your script file, run ?mpg to learn more about this dataset. To run the code, highlight it and then click Run. (shortcut keys: Mac: command + Enter, Windows: CTRL + Enter)

Exercise #2: Run head(mpg) to see the first few rows of the data.

head(mpg)

## # A tibble: 6 x 11
##   manufacturer model displ  year   cyl      trans   drv   cty   hwy    fl
##          <chr> <chr> <dbl> <int> <int>      <chr> <chr> <int> <int> <chr>
## 1         audi    a4   1.8  1999     4   auto(l5)     f    18    29     p
## 2         audi    a4   1.8  1999     4 manual(m5)     f    21    29     p
## 3         audi    a4   2.0  2008     4 manual(m6)     f    20    31     p
## 4         audi    a4   2.0  2008     4   auto(av)     f    21    30     p
## 5         audi    a4   2.8  1999     6   auto(l5)     f    16    26     p
## 6         audi    a4   2.8  1999     6 manual(m5)     f    18    26     p
## # ... with 1 more variables: class <chr>

Exercise #3: ggplot syntax

The graph below uses ggplot2 to look for correlation between a car’s engine displacement and highway mileage.

• library(ggplot2): loads the ggplot2 library

• ggplot() : function that tells R that you want to make a graph with ggplot

• data = mpg : says that you want to use the mpg dataset (sample data that comes with R)

• geom_point(): function that says you want to make a scatterplot

• mapping = aes(): function that allows you to map data variables to X and Y axes

**Run the following code in your script file:**

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

Exercise #4: Practice

Make a scatterplot with cyl mapped to the x-axis and hwy mapped to the y-axis.

Solution to #4

ggplot(data= mpg) + geom_point(mapping = aes(x=cyl, y=hwy))

Exercise #5: Mapping a variable to color

Make a scatterplot of disp=x and hwy=y with class mapped to the color aesthetic. Run:

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

Exercise #6: Make the same scatterplot as the previous example, but map drv to color.

Solution to #6

The type of drive system the car has (4-wheel, rear-wheel, and front-wheel) is mapped to color.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = drv))

Exercise #7: Aesthetic parameters

Variables can be mapped to the following aesthetic parameters. If you are publishing in b/w, and can’t use color, you might want to use size or shape:

• color
• size
• shape
• alpha - transparency

Substitute another aesthetic in place of color. Run the code:

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = drv))

Exercise #8: Faceting

Facets are a way to create multiple smaller charts, or subplots, based on a variable. Run this code to see what faceting does:

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

Exercise #9: Practice Faceting.

Substitute class for another variable in the dataset. Ex: trans, drive, or cyl

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

Exercise 10: facet_grid

Facet grids allow for an extra dimension of faceting. Run this code in your script to see what facet_grid() does:

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ cyl)

Apply what you have learned

Now create a new scatter plot with the dataset diamonds using ggplot2. Refer to previous code examples for assistance.

head(diamonds)

## # A tibble: 6 x 10
##   carat       cut color clarity depth table price     x     y     z
##   <dbl>     <ord> <ord>   <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1  0.23     Ideal     E     SI2  61.5    55   326  3.95  3.98  2.43
## 2  0.21   Premium     E     SI1  59.8    61   326  3.89  3.84  2.31
## 3  0.23      Good     E     VS1  56.9    65   327  4.05  4.07  2.31
## 4  0.29   Premium     I     VS2  62.4    58   334  4.20  4.23  2.63
## 5  0.31      Good     J     SI2  63.3    58   335  4.34  4.35  2.75
## 6  0.24 Very Good     J    VVS2  62.8    57   336  3.94  3.96  2.48

Example:

ggplot(data = diamonds) + 
  geom_point(mapping = aes(x = carat, y = price, color=cut)) + facet_wrap(~cut, nrow=2)